





'''simple docstring'''



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


_SCREAMING_SNAKE_CASE       =     logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE       =     {
    '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}






class 				__lowercase							(   snake_case__    ):







	'''simple docstring'''

	a     :					Union[str, Any]           =							'''blenderbot-small'''
	a     :					Dict           =							['''past_key_values''']
	a     :					Optional[int]           =							{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


	def __init__(self  ,_lowerCamelCase=50265  ,_lowerCamelCase=512  ,_lowerCamelCase=8  ,_lowerCamelCase=2048  ,_lowerCamelCase=16  ,_lowerCamelCase=8  ,_lowerCamelCase=2048  ,_lowerCamelCase=16  ,_lowerCamelCase=0.0  ,_lowerCamelCase=0.0  ,_lowerCamelCase=True  ,_lowerCamelCase=True  ,_lowerCamelCase="gelu"  ,_lowerCamelCase=512  ,_lowerCamelCase=0.1  ,_lowerCamelCase=0.0  ,_lowerCamelCase=0.0  ,_lowerCamelCase=0.0_2  ,_lowerCamelCase=1  ,_lowerCamelCase=False  ,_lowerCamelCase=0  ,_lowerCamelCase=1  ,_lowerCamelCase=2  ,_lowerCamelCase=2  ,**_lowerCamelCase  ,)				->				str:







				'''simple docstring'''





				__lowercase					    =				vocab_size
				__lowercase					    =				max_position_embeddings
				__lowercase					    =				d_model
				__lowercase					    =				encoder_ffn_dim
				__lowercase					    =				encoder_layers
				__lowercase					    =				encoder_attention_heads
				__lowercase					    =				decoder_ffn_dim
				__lowercase					    =				decoder_layers
				__lowercase					    =				decoder_attention_heads
				__lowercase					    =				dropout
				__lowercase					    =				attention_dropout
				__lowercase					    =				activation_dropout
				__lowercase					    =				activation_function
				__lowercase					    =				init_std
				__lowercase					    =				encoder_layerdrop
				__lowercase					    =				decoder_layerdrop
				__lowercase					    =				use_cache
				__lowercase					    =				encoder_layers
				__lowercase					    =				scale_embedding  # scale factor will be sqrt(d_model) if True

				super().__init__(
				    pad_token_id=_UpperCAmelCase  ,bos_token_id=_UpperCAmelCase  ,eos_token_id=_UpperCAmelCase  ,is_encoder_decoder=_UpperCAmelCase  ,decoder_start_token_id=_UpperCAmelCase  ,forced_eos_token_id=_UpperCAmelCase  ,**_UpperCAmelCase  ,)






class 				__lowercase							(   snake_case__    ):







	'''simple docstring'''

	@property
	def     _UpperCAmelCase  (self			)				->				Mapping[str, Mapping[int, str]]:







				'''simple docstring'''





				if self.task in ["default", "seq2seq-lm"]:
							__lowercase					    =				OrderedDict(
							    [
							        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
							        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
							    ]			)

							if self.use_past:
										__lowercase					    =				{0: '''batch'''}
										__lowercase					    =				{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
							else:
										__lowercase					    =				{0: '''batch''', 1: '''decoder_sequence'''}
										__lowercase					    =				{0: '''batch''', 1: '''decoder_sequence'''}

							if self.use_past:
										self.fill_with_past_key_values_(_UpperCAmelCase  ,direction='''inputs'''			)
				elif self.task == "causal-lm":
							# TODO: figure this case out.
							__lowercase					    =				OrderedDict(
							    [
							        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
							        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
							    ]			)
							if self.use_past:
										__lowercase    ,		__lowercase					    =				self.num_layers
										for i in range(_UpperCAmelCase			):
													__lowercase					    =				{0: '''batch''', 2: '''past_sequence + sequence'''}
													__lowercase					    =				{0: '''batch''', 2: '''past_sequence + sequence'''}
				else:
							__lowercase					    =				OrderedDict(
							    [
							        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
							        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
							        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
							        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
							    ]			)

				return common_inputs


	@property
	def     _UpperCAmelCase  (self			)				->				Mapping[str, Mapping[int, str]]:







				'''simple docstring'''





				if self.task in ["default", "seq2seq-lm"]:
							__lowercase					    =				super().outputs
				else:
							__lowercase					    =				super(_UpperCAmelCase  ,self			).outputs
							if self.use_past:
										__lowercase    ,		__lowercase					    =				self.num_layers
										for i in range(_UpperCAmelCase			):
													__lowercase					    =				{0: '''batch''', 2: '''past_sequence + sequence'''}
													__lowercase					    =				{0: '''batch''', 2: '''past_sequence + sequence'''}
				return common_outputs


	def     _UpperCAmelCase  (self  ,_lowerCamelCase  ,_lowerCamelCase = -1  ,_lowerCamelCase = -1  ,_lowerCamelCase = False  ,_lowerCamelCase = None  ,)				->				Mapping[str, Any]:







				'''simple docstring'''





				__lowercase					    =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    _UpperCAmelCase  ,_UpperCAmelCase  ,_UpperCAmelCase  ,_UpperCAmelCase  ,_UpperCAmelCase			)

				# Generate decoder inputs
				__lowercase					    =				seq_length if not self.use_past else 1
				__lowercase					    =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    _UpperCAmelCase  ,_UpperCAmelCase  ,_UpperCAmelCase  ,_UpperCAmelCase  ,_UpperCAmelCase			)
				__lowercase					    =				{f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
				__lowercase					    =				dict(**_UpperCAmelCase  ,**_UpperCAmelCase			)

				if self.use_past:
							if not is_torch_available():
										raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''			)
							else:
										import torch
							__lowercase    ,		__lowercase					    =				common_inputs['''input_ids'''].shape
							__lowercase					    =				common_inputs['''decoder_input_ids'''].shape[1]
							__lowercase    ,		__lowercase					    =				self.num_attention_heads
							__lowercase					    =				(
							    batch,
							    num_encoder_attention_heads,
							    encoder_seq_length,
							    self._config.hidden_size // num_encoder_attention_heads,
							)
							__lowercase					    =				decoder_seq_length + 3
							__lowercase					    =				(
							    batch,
							    num_decoder_attention_heads,
							    decoder_past_length,
							    self._config.hidden_size // num_decoder_attention_heads,
							)

							__lowercase					    =				torch.cat(
							    [common_inputs['''decoder_attention_mask'''], torch.ones(_UpperCAmelCase  ,_UpperCAmelCase			)]  ,dim=1			)

							__lowercase					    =				[]
							# If the number of encoder and decoder layers are present in the model configuration, both are considered
							__lowercase    ,		__lowercase					    =				self.num_layers
							__lowercase					    =				min(_UpperCAmelCase  ,_UpperCAmelCase			)
							__lowercase					    =				max(_UpperCAmelCase  ,_UpperCAmelCase			) - min_num_layers
							__lowercase					    =				'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

							for _ in range(_UpperCAmelCase			):
										common_inputs["past_key_values"].append(
										    (
										        torch.zeros(_UpperCAmelCase			),
										        torch.zeros(_UpperCAmelCase			),
										        torch.zeros(_UpperCAmelCase			),
										        torch.zeros(_UpperCAmelCase			),
										    )			)
							# TODO: test this.
							__lowercase					    =				encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
							for _ in range(_UpperCAmelCase  ,_UpperCAmelCase			):
										common_inputs["past_key_values"].append((torch.zeros(_UpperCAmelCase			), torch.zeros(_UpperCAmelCase			))			)
				return common_inputs


	def     _UpperCAmelCase  (self  ,_lowerCamelCase  ,_lowerCamelCase = -1  ,_lowerCamelCase = -1  ,_lowerCamelCase = False  ,_lowerCamelCase = None  ,)				->				Mapping[str, Any]:







				'''simple docstring'''





				__lowercase					    =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    _UpperCAmelCase  ,_UpperCAmelCase  ,_UpperCAmelCase  ,_UpperCAmelCase  ,_UpperCAmelCase			)

				if self.use_past:
							if not is_torch_available():
										raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''			)
							else:
										import torch
							__lowercase    ,		__lowercase					    =				common_inputs['''input_ids'''].shape
							# Not using the same length for past_key_values
							__lowercase					    =				seqlen + 2
							__lowercase    ,		__lowercase					    =				self.num_layers
							__lowercase    ,		__lowercase					    =				self.num_attention_heads
							__lowercase					    =				(
							    batch,
							    num_encoder_attention_heads,
							    past_key_values_length,
							    self._config.hidden_size // num_encoder_attention_heads,
							)

							__lowercase					    =				common_inputs['''attention_mask'''].dtype
							__lowercase					    =				torch.cat(
							    [common_inputs['''attention_mask'''], torch.ones(_UpperCAmelCase  ,_UpperCAmelCase  ,dtype=_UpperCAmelCase			)]  ,dim=1			)
							__lowercase					    =				[
							    (torch.zeros(_UpperCAmelCase			), torch.zeros(_UpperCAmelCase			)) for _ in range(_UpperCAmelCase			)
							]
				return common_inputs


	def     _UpperCAmelCase  (self  ,_lowerCamelCase  ,_lowerCamelCase = -1  ,_lowerCamelCase = -1  ,_lowerCamelCase = False  ,_lowerCamelCase = None  ,)				->				Mapping[str, Any]:







				'''simple docstring'''





				__lowercase					    =				compute_effective_axis_dimension(
				    _UpperCAmelCase  ,fixed_dimension=OnnxConfig.default_fixed_batch  ,num_token_to_add=0			)

				# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
				__lowercase					    =				tokenizer.num_special_tokens_to_add(_UpperCAmelCase			)
				__lowercase					    =				compute_effective_axis_dimension(
				    _UpperCAmelCase  ,fixed_dimension=OnnxConfig.default_fixed_sequence  ,num_token_to_add=_UpperCAmelCase			)

				# Generate dummy inputs according to compute batch and sequence
				__lowercase					    =				[''' '''.join([tokenizer.unk_token]			) * seq_length] * batch_size
				__lowercase					    =				dict(tokenizer(_UpperCAmelCase  ,return_tensors=_UpperCAmelCase			)			)
				return common_inputs


	def     _UpperCAmelCase  (self  ,_lowerCamelCase  ,_lowerCamelCase = -1  ,_lowerCamelCase = -1  ,_lowerCamelCase = False  ,_lowerCamelCase = None  ,)				->				Mapping[str, Any]:







				'''simple docstring'''





				if self.task in ["default", "seq2seq-lm"]:
							__lowercase					    =				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
							    _UpperCAmelCase  ,batch_size=_UpperCAmelCase  ,seq_length=_UpperCAmelCase  ,is_pair=_UpperCAmelCase  ,framework=_UpperCAmelCase			)

				elif self.task == "causal-lm":
							__lowercase					    =				self._generate_dummy_inputs_for_causal_lm(
							    _UpperCAmelCase  ,batch_size=_UpperCAmelCase  ,seq_length=_UpperCAmelCase  ,is_pair=_UpperCAmelCase  ,framework=_UpperCAmelCase			)
				else:
							__lowercase					    =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    _UpperCAmelCase  ,batch_size=_UpperCAmelCase  ,seq_length=_UpperCAmelCase  ,is_pair=_UpperCAmelCase  ,framework=_UpperCAmelCase			)

				return common_inputs


	def     _UpperCAmelCase  (self  ,_lowerCamelCase  ,_lowerCamelCase  ,_lowerCamelCase  ,_lowerCamelCase			)				->				Dict:







				'''simple docstring'''





				if self.task in ["default", "seq2seq-lm"]:
							__lowercase					    =				super()._flatten_past_key_values_(_UpperCAmelCase  ,_UpperCAmelCase  ,_UpperCAmelCase  ,_UpperCAmelCase			)
				else:
							__lowercase					    =				super(_UpperCAmelCase  ,self			)._flatten_past_key_values_(
							    _UpperCAmelCase  ,_UpperCAmelCase  ,_UpperCAmelCase  ,_UpperCAmelCase			)










'''simple docstring'''

from __future__ import annotations

SCREAMING_SNAKE_CASE__     :						List[Any]	=  1.6021e-19  # units = C






def 				a  (     UpperCamelCase_    :    float      ,					UpperCamelCase_    :    float      ,					UpperCamelCase_    :    float      ,					)       ->	tuple[str, float]:
		if (conductivity, electron_conc, mobility).count(0			) != 1:
				raise ValueError('You cannot supply more or less than 2 values'			)
		elif conductivity < 0:
				raise ValueError('Conductivity cannot be negative'			)
		elif electron_conc < 0:
				raise ValueError('Electron concentration cannot be negative'			)
		elif mobility < 0:
				raise ValueError('mobility cannot be negative'			)
		elif conductivity == 0:
				return (
				    "conductivity",
				    mobility * electron_conc * ELECTRON_CHARGE,
				)
		elif electron_conc == 0:
				return (
				    "electron_conc",
				    conductivity / (mobility * ELECTRON_CHARGE),
				)
		else:
				return (
				    "mobility",
				    conductivity / (electron_conc * ELECTRON_CHARGE),
				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()




